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Poly-S: Analyzing and Improving Polytropon for Data-Efficient Multi-Task Learning
Lucas Page-Caccia · Edoardo Maria Ponti · Liyuan Liu · Matheus Pereira · Nicolas Le Roux · Alessandro Sordoni
Event URL: https://openreview.net/forum?id=g8xkPAiIlG8 »

Polytropon learns a set of modular skills, which can be re-combined and fine-tuned on novel tasks with limited data. In this paper, we first investigate what makes this method successful. Specifically, we extend the evaluation benchmark to include more datasets and design a series of controlled experiments to isolate the impact of different components.We then propose a new method, Poly-S, which allows for a more fine-grained control over the combination of skills, with no additional cost in compute at inference time. We evaluate Poly-S on three multi-task NLP benchmarks, and observe improvements over strong baselines.

Author Information

Lucas Page-Caccia (McGill University)
Edoardo Maria Ponti (University of Edinburgh)
Liyuan Liu (University of Illinois, Urbana Champaign)
Matheus Pereira (Microsoft)
Nicolas Le Roux (Microsoft Research)
Alessandro Sordoni (Microsoft Research Montreal)

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